The present invention relates to the field of data analysis.
The concept of providing a user with suggestions or recommendations related to an item they have viewed has become a common element of many types of data systems. For example, many retail Web sites analyze the history of products a shopper has viewed or purchased to recommend other related products that the shopper may be interested in purchasing. In a more traditional data system, recommendations for data files or documents related to a user's past viewing history or a set of search criteria are determined and presented to the user.
While this technique is often helpful, conventional recommendation engines are limited to basing their recommendations for the related products or documents, collectively referred to as data artifacts, at the artifact-level. That is, metadata about the data artifact, such as subject and author, are as the basis for providing recommendations for data artifacts related to the user's history or criteria. This results in the user having to read through each recommended data artifact in order to ascertain what content is newsworthy or of value to their current task, decreasing a user's certainty in the applicability of the recommendations.